[DOCS] Updates get trained models API docs (#79372)

* [DOCS] Updates get trained models API docs.

* [DOCS] Reviews get trained models related definitions in ml-shared.

docs/reference/ml/df-analytics/apis/get-trained-models.asciidoc

@@ -102,7 +102,7 @@ ascending order.
 ====
 `created_by`:::
 (string)
-Information on the creator of the trained model.
+The creator of the trained model.
 
 `create_time`:::
 (<<time-units,time units>>)
@@ -110,11 +110,10 @@ The time when the trained model was created.
 
 `default_field_map` :::
 (object)
-A string to string object that contains the default field map to use
-when inferring against the model. For example, data frame analytics
-may train the model on a specific multi-field `foo.keyword`.
-The analytics job would then supply a default field map entry for
-`"foo" : "foo.keyword"`.
+A string object that contains the default field map to use when inferring 
+against the model. For example, {dfanalytics} may train the model on a specific 
+multi-field `foo.keyword`. The analytics job would then supply a default field 
+map entry for `"foo" : "foo.keyword"`.
 +
 Any field map described in the inference configuration takes precedence.
 
@@ -133,8 +132,8 @@ The estimated number of operations to use the trained model.
 `inference_config`:::
 (object)
 The default configuration for inference. This can be either a `regression`
-or `classification` configuration. It must match the underlying
-`definition.trained_model`'s `target_type`.
+or `classification` configuration. It must match the `target_type` of the 
+underlying `definition.trained_model`.
 +
 .Properties of `inference_config`
 [%collapsible%open]

docs/reference/ml/ml-shared.asciidoc

@@ -897,8 +897,8 @@ end::inference-config-classification-num-top-classes[]
 
 tag::inference-config-classification-num-top-feature-importance-values[]
 Specifies the maximum number of
-{ml-docs}/ml-feature-importance.html[{feat-imp}] values per document. By
-default, it is zero and no {feat-imp} calculation occurs.
+{ml-docs}/ml-feature-importance.html[{feat-imp}] values per document. Defaults 
+to 0 which means no {feat-imp} calculation occurs.
 end::inference-config-classification-num-top-feature-importance-values[]
 
 tag::inference-config-classification-top-classes-results-field[]
@@ -908,7 +908,7 @@ end::inference-config-classification-top-classes-results-field[]
 
 tag::inference-config-classification-prediction-field-type[]
 Specifies the type of the predicted field to write.
-Acceptable values are: `string`, `number`, `boolean`. When `boolean` is provided
+Valid values are: `string`, `number`, `boolean`. When `boolean` is provided 
 `1.0` is transformed to `true` and `0.0` to `false`.
 end::inference-config-classification-prediction-field-type[]
 
@@ -921,8 +921,8 @@ BERT-style tokenization is to be performed with the enclosed settings.
 end::inference-config-nlp-tokenization-bert[]
 
 tag::inference-config-nlp-tokenization-bert-do-lower-case[]
-Should the tokenization lower case the text sequence when building
-the tokens.
+Specifies if the tokenization lower case the text sequence when building the 
+tokens.
 end::inference-config-nlp-tokenization-bert-do-lower-case[]
 
 tag::inference-config-nlp-tokenization-bert-with-special-tokens[]
@@ -935,29 +935,29 @@ Tokenize with special tokens. The tokens typically included in BERT-style tokeni
 end::inference-config-nlp-tokenization-bert-with-special-tokens[]
 
 tag::inference-config-nlp-tokenization-bert-max-sequence-length[]
-The maximum number of tokens allowed to be output by the tokenizer.
+Specifies the maximum number of tokens allowed to be output by the tokenizer. 
 The default for BERT-style tokenization is `512`.
 end::inference-config-nlp-tokenization-bert-max-sequence-length[]
 
 tag::inference-config-nlp-vocabulary[]
-The configuration for retreiving the model's vocabulary. The vocabulary is then
-used at inference time. This information is usually provided automatically by
-storing vocabulary in a known, internally managed index.
+The configuration for retreiving the vocabulary of the model. The vocabulary is 
+then used at inference time. This information is usually provided automatically 
+by storing vocabulary in a known, internally managed index.
 end::inference-config-nlp-vocabulary[]
 
 tag::inference-config-nlp-fill-mask[]
-Configuration for a fill_mask NLP task. The fill_mask task works with models
-optimized for a fill mask action. For example, for BERT models, the following
-text may be provided: "The capital of France is [MASK].". The response indicates
-the value most likely to replace `[MASK]`. In this instance, the
-most probable token is `paris`.
+Configuration for a fill_mask natural language processing (NLP) task. The 
+fill_mask task works with models optimized for a fill mask action. For example, 
+for BERT models, the following text may be provided: "The capital of France is 
+[MASK].". The response indicates the value most likely to replace `[MASK]`. In 
+this instance, the most probable token is `paris`.
 end::inference-config-nlp-fill-mask[]
 
 tag::inference-config-ner[]
 Configures a named entity recognition (NER) task. NER is a special case of token
 classification. Each token in the sequence is classified according to the
 provided classification labels. Currently, the NER task requires the
-`classification_labels` Inside-Outside-Beginning formatted labels. Only
+`classification_labels` Inside-Outside-Beginning (IOB) formatted labels. Only
 person, organization, location, and miscellaneous are supported.
 end::inference-config-ner[]
 
@@ -977,8 +977,8 @@ end::inference-config-text-classification[]
 tag::inference-config-text-embedding[]
 Text embedding takes an input sequence and transforms it into a vector of
 numbers. These embeddings capture not simply tokens, but semantic meanings and
-context. These embeddings can then be used in a <<dense-vector,dense vector>>
-field for powerful insights.
+context. These embeddings can be used in a <<dense-vector,dense vector>> field 
+for powerful insights.
 end::inference-config-text-embedding[]
 
 tag::inference-config-regression-num-top-feature-importance-values[]
@@ -1005,8 +1005,8 @@ it is possible to adjust the labels to classify. This makes this type of model
 and task exceptionally flexible.
 +
 --
-If consistently classifying the same labels, it may be better to use a fine turned
-text classification model.
+If consistently classifying the same labels, it may be better to use a 
+fine-tuned text classification model.
 --
 end::inference-config-zero-shot-classification[]
 
@@ -1021,9 +1021,11 @@ end::inference-config-zero-shot-classification-classification-labels[]
 
 tag::inference-config-zero-shot-classification-hypothesis-template[]
 This is the template used when tokenizing the sequences for classification.
-
++
+--
 The labels replace the `{}` value in the text. The default value is:
 `This example is {}.`
+--
 end::inference-config-zero-shot-classification-hypothesis-template[]
 
 tag::inference-config-zero-shot-classification-labels[]
@@ -1033,11 +1035,8 @@ end::inference-config-zero-shot-classification-labels[]
 
 tag::inference-config-zero-shot-classification-multi-label[]
 Indicates if more than one `true` label is possible given the input.
-
 This is useful when labeling text that could pertain to more than one of the
-input labels.
-
-Defaults to `false`.
+input labels. Defaults to `false`.
 end::inference-config-zero-shot-classification-multi-label[]
 
 tag::inference-metadata-feature-importance-feature-name[]